class_docstr: The performance transformer transforms performances into model outputs for
  supervised learning as well as model outputs to performance objects.
class_docstr_tok: ["The","performance","transformer","transforms","performances","into","model","outputs","for","supervised","learning","as","well","as","model","outputs","to","performance","objects","."]
docstr: Transforms the provided NumPy arrays back into performance objects
  according to the fitted transformer.
  
  Args:
      y: A NumPy array of shape [N, K] of performances (N: number of performances, K: number
          of performance metrics).
  
  Returns:
      The performance objects.
docstr_tok: ["Transforms","the","provided","NumPy","arrays","back","into","performance","objects","according","to","the","fitted","transformer",".","Args",":","y",":","A","NumPy","array","of","shape","[","N",",","K","]","of","performances","(","N",":","number","of","performances",",","K",":","number","of","performance","metrics",")",".","Returns",":","The","performance","objects","."]
code: def inverse_transform(
      self, y: npt.NDArray[np.float32]
  ) -> list[Performance]:
      """
      Transforms the provided NumPy arrays back into performance objects
      according to the fitted transformer.
  
      Args:
          y: A NumPy array of shape [N, K] of performances (N: number of performances, K: number
              of performance metrics).
  
      Returns:
          The performance objects.
      """
      return self.encoder.inverse_transform(y)
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/surrogate/transformers/performance.py
imports: from __future__ import annotations
  from typing import Any, List, Optional
  import numpy
  import numpy.typing
  import pandas
  from sklearn.base import TransformerMixin
  from tsbench.evaluations.metrics import Performance
label_desc: Use image node_id 5 for calling the PerformanceTransformer obj's underlying member method code with example usage: obj.inverse_transform(y) and returns: self